Crowd Forecasting at Venues with Microblog Posts Referring to Future Events

Ryotaro Tsukada, Haosen Zhan, Shonosuke Ishiwatari, Masashi Toyoda, Kazutoshi Umemoto, Haichuan Shang, Koji Zettsu

Abstract: Large events with many attendees cause congestion in the traffic network around the venue. To avoid accidents or delays due to this kind of unexpected congestion, it is important to predict the level of congestion in advance of the event. This study aimed to forecast congestion triggered by large events. However, historical congestion information alone is insufficient to forecast congestion at large venues when non-recurrent events are held there. To address this problem, we utilize microblog posts that refer to future events as an indicator of event attendance. We propose a regression model that is trained with microblog posts and historical congestion information to accurately forecast congestion at large venues. Experiments on next 24-hour congestion forecasting using real-world traffic and Twitter data demonstrate that our model reduces the prediction errors over those of the baseline models (autoregressive and long short term memory) by 20% - 50%.